6th International Conference on Image Processing and Its Applications 1997
DOI: 10.1049/cp:19970981
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Phase unwrapping in 3-D shape measurement using artificial neural networks

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Cited by 5 publications
(6 citation statements)
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“…The Al stochastic search is a group of techniques that uses probabilistic methods relying on randomized decisions, including algorithms such as simulated annealing, neural networks, and genetic algorithms [15]. Several attempts have been made to solve the phase unwrapping problem using Al stochastic search techniques [16][17][18][19][20][21][22][23][24]; yet these algorithms have not been challenged by the variety of vexing phase unwrapping situations occurring in actual data analysis, including steep spatial gradients, but rather usually given only simple scenarios [15]. Specifically, artificial neural networks have the potential to yield a robust solution to the phase unwrapping problem, since they can learn the characteristics of the input data, and if trained properly, use this information to unwrap the phase more accurately while ignoring noise.…”
Section: Introductionmentioning
confidence: 99%
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“…The Al stochastic search is a group of techniques that uses probabilistic methods relying on randomized decisions, including algorithms such as simulated annealing, neural networks, and genetic algorithms [15]. Several attempts have been made to solve the phase unwrapping problem using Al stochastic search techniques [16][17][18][19][20][21][22][23][24]; yet these algorithms have not been challenged by the variety of vexing phase unwrapping situations occurring in actual data analysis, including steep spatial gradients, but rather usually given only simple scenarios [15]. Specifically, artificial neural networks have the potential to yield a robust solution to the phase unwrapping problem, since they can learn the characteristics of the input data, and if trained properly, use this information to unwrap the phase more accurately while ignoring noise.…”
Section: Introductionmentioning
confidence: 99%
“…Specifically, artificial neural networks have the potential to yield a robust solution to the phase unwrapping problem, since they can learn the characteristics of the input data, and if trained properly, use this information to unwrap the phase more accurately while ignoring noise. In the past, several attempts have been made for applying phase unwrapping using neural networks both in one-dimension [18,19] and in two dimensions [20][21][22][23][24] for various applications; yet up until recently, all suggested neural networks were shallow, consisting of less than 5 layers. In the past couple of years, the concept of deep learning has emerged as a gold-standard solution to many types of problems in endless fields [25], where the revolution lies in the use of hundreds of hidden layers, consisting of millions of parameters, which is enabled by recent computational advancements.…”
Section: Introductionmentioning
confidence: 99%
“…Forward propagation in neural networks is simple, and an entire image can be unwrapped in a fraction of a second. While all previous two-dimensional phase unwrapping methods [1,[6][7][8][9][10][11][12][13][14][15][16][17][18] were designed for general phase images, the neural network method can learn specific types of images. Other methods produce comparable results, but many of them take much longer than the proposed method.…”
Section: Discussionmentioning
confidence: 99%
“…Besides these methods, there are several other approaches including cellular automata [12], polynomial estimation [13], and fractals [14]. There have also been a few attempts at phase unwrapping with neural networks in one-dimension [17,18] and two dimensions [15,16], but the two-dimensional techniques have taken several hours to unwrap even simple images.…”
Section: Phase Unwrapping In Odt Imagesmentioning
confidence: 99%
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